Leveraging V2X for Collaborative HD Maps Construction Using Scene Graph Generation
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909546823286784 |
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| author | Elghazaly, Gamal Frank, Raphael |
| author_facet | Elghazaly, Gamal Frank, Raphael |
| contents | High-Definition (HD) maps play a crucial role in autonomous vehicle navigation, complementing onboard perception sensors for improved accuracy and safety. Traditional HD map generation relies on dedicated mapping vehicles, which are costly and fail to capture real-time infrastructure changes. This paper presents HDMapLaneNet, a novel framework leveraging V2X communication and Scene Graph Generation to collaboratively construct a localized geometric layer of HD maps. The approach extracts lane centerlines from front-facing camera images, represents them as graphs, and transmits the data for global aggregation to the cloud via V2X. Preliminary results on the nuScenes dataset demonstrate superior association prediction performance compared to a state-of-the-art method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_10127 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Leveraging V2X for Collaborative HD Maps Construction Using Scene Graph Generation Elghazaly, Gamal Frank, Raphael Computer Vision and Pattern Recognition High-Definition (HD) maps play a crucial role in autonomous vehicle navigation, complementing onboard perception sensors for improved accuracy and safety. Traditional HD map generation relies on dedicated mapping vehicles, which are costly and fail to capture real-time infrastructure changes. This paper presents HDMapLaneNet, a novel framework leveraging V2X communication and Scene Graph Generation to collaboratively construct a localized geometric layer of HD maps. The approach extracts lane centerlines from front-facing camera images, represents them as graphs, and transmits the data for global aggregation to the cloud via V2X. Preliminary results on the nuScenes dataset demonstrate superior association prediction performance compared to a state-of-the-art method. |
| title | Leveraging V2X for Collaborative HD Maps Construction Using Scene Graph Generation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.10127 |